Offchain Public Keys | Proof is in the Pudding Session 07

This episode reveals a pragmatic, low-cost method for verifying AI models using Offchain Public Keys, presenting a crucial alternative to complex cryptographic solutions for investors tracking the intersection of AI and Web3.

Introducing the "Proof is in the Pudding" Concept

The discussion begins by introducing the "Proof is in the Pudding" framework, an analogy for verifying the integrity of an AI model's output. Hart explains that the core challenge is proving that a specific, known AI model—the "pudding"—was responsible for generating a particular result—the "proof." This sets the stage for a deeper exploration into methods that can cryptographically link an AI's output back to its source model without requiring computationally expensive on-chain verification.

The Mechanics of Offchain Public Key Attestation

Hart details a novel approach using Offchain Public Keys, which are cryptographic keys not stored on a blockchain, to create verifiable attestations. An attestation is a digitally signed statement that confirms a piece of information is true. In this context, the AI model provider signs the model's output with a private key, and anyone with the corresponding public key can verify that the signature is authentic. This proves the output originated from that specific model version.

• The process involves the model provider generating a key pair (public and private).
• The public key is distributed to users who need to verify the model's outputs.
• When the model produces a result, it is signed with the private key.
• Hart notes, "The signature acts as an undeniable link. If the verification passes, you know with cryptographic certainty that the model you think ran is the one that actually ran."

Strategic Implication: This method offers a lightweight, low-cost solution for AI model verification, a critical component for building trust in decentralized AI applications. Investors should monitor projects adopting this approach as it lowers the barrier to entry for verifiable AI.

Practical Applications in AI Model Verification

Anna shifts the conversation to real-world applications, emphasizing the importance of version control and accountability for AI models. She explains that as models are constantly updated, users and developers need a reliable way to confirm which version produced a specific result, especially for applications in finance or data analysis where precision is critical.

• This off-chain method allows developers to prove a bug or an unexpected output was tied to a specific, now-updated model version.
• It also provides end-users with a guarantee that they are interacting with the genuine, intended AI service and not a counterfeit or manipulated version.

Actionable Insight: For researchers, this highlights a growing demand for tools that manage AI model provenance and versioning. Solutions that integrate simple cryptographic attestations into the MLOps (Machine Learning Operations) pipeline represent a significant area of opportunity.

Analyzing Centralization and Security Trade-offs

Sam introduces a critical perspective, questioning the centralization risks inherent in this model. He points out that the entire system's security hinges on the model provider's ability to secure the private key. If the key is compromised, the entire verification system collapses, allowing malicious actors to sign fraudulent outputs.

• The discussion touches on the need for a trusted entity or a secure system to manage and distribute the public keys.
• This reliance on a central party for key management contrasts with fully decentralized verification methods.

Strategic Consideration: While this approach is efficient, its centralized trust model is a significant drawback. Investors should assess how projects implementing this system mitigate key management risks, as this represents a primary vector for failure or attack.

A Comparative Look: zkML, MPC, and Offchain Attestation

The team compares the Offchain Public Key method with more complex cryptographic solutions like zkML and MPC.

• zkML (Zero-Knowledge Machine Learning) is a technology that allows for the verification of an AI model's computation without revealing the underlying data or the model itself, offering maximum privacy.
• MPC (Multi-Party Computation) enables multiple parties to jointly compute a function over their inputs while keeping those inputs private.

Anna clarifies that while zkML and MPC offer superior privacy and decentralization, they are computationally intensive and expensive. The off-chain attestation method is presented as a pragmatic, immediate solution that prioritizes low cost and ease of implementation over absolute trustlessness.

Actionable Insight: The market is not one-size-fits-all. Investors should recognize a spectrum of verification needs. While zkML represents the long-term goal for high-stakes, private AI, simpler attestation methods are likely to gain near-term adoption for less sensitive applications due to their cost-effectiveness and simplicity.

Conclusion: A Pragmatic Step Toward Verifiable AI

This episode highlights Offchain Public Key attestation as a practical, low-cost method for verifying AI model outputs. While it introduces centralization risks, its simplicity makes it a viable near-term solution. Investors and researchers should monitor hybrid approaches that balance cryptographic security with real-world usability and cost.